import gradio as gr
from inference import Inference
import PIL
from PIL import Image
import pandas as pd
import random
from rdkit import Chem
from rdkit.Chem import Draw
from rdkit.Chem.Draw import IPythonConsole
import shutil

class DrugGENConfig:
    submodel='DrugGEN'
    act='relu'
    max_atom=45
    dim=32
    depth=1
    heads=8
    mlp_ratio=3
    dropout=0.
    features=False
    inference_sample_num=1000
    inf_batch_size=1
    protein_data_dir='data/akt'
    drug_index='data/drug_smiles.index'
    drug_data_dir='data/akt'
    mol_data_dir='data'
    log_dir='experiments/logs'
    model_save_dir='experiments/models'
    inference_model="experiments/models/DrugGEN"
    sample_dir='experiments/samples'
    result_dir="experiments/tboard_output"
    dataset_file="chembl45_train.pt"
    drug_dataset_file="akt_train.pt"
    raw_file='data/chembl_train.smi'
    drug_raw_file="data/akt_train.smi"
    inf_dataset_file="chembl45_test.pt"
    inf_drug_dataset_file='akt_test.pt'
    inf_raw_file='data/chembl_test.smi'
    inf_drug_raw_file="data/akt_test.smi"
    log_sample_step=1000
    set_seed=False
    seed=1


class NoTargetConfig(DrugGENConfig):
    submodel="NoTarget"
    dim=128
    inference_model="experiments/models/NoTarget"


model_configs = {
    "DrugGEN": DrugGENConfig(),
    "NoTarget": NoTargetConfig(),
}



def function(model_name: str, mol_num: int, seed: int) -> tuple[PIL.Image, pd.DataFrame, str]:
    '''
    Returns:
    image, score_df, file path
    '''

    config = model_configs[model_name]
    config.inference_sample_num = mol_num
    config.seed = seed

    inferer = Inference(config)
    scores = inferer.inference() # create scores_df out of this

    score_df = pd.DataFrame(scores, index=[0])

    output_file_path = f'experiments/inference/{model_name}/inference_drugs.txt'

    import os
    new_path = f'{model_name}_denovo_mols.smi'
    os.rename(output_file_path, new_path)

    with open(new_path) as f:
        inference_drugs = f.read()

    generated_molecule_list = inference_drugs.split("\n")

    rng = random.Random(seed)
    selected_molecules = rng.choices(generated_molecule_list,k=12)
    selected_molecules = [Chem.MolFromSmiles(mol) for mol in selected_molecules]

    drawOptions = Draw.rdMolDraw2D.MolDrawOptions()
    drawOptions.prepareMolsBeforeDrawing = False
    drawOptions.bondLineWidth = 0.5

    molecule_image = Draw.MolsToGridImage(
        selected_molecules,
        molsPerRow=3,
        subImgSize=(400, 400),
        maxMols=len(selected_molecules),
        # legends=None,
        returnPNG=False,
        drawOptions=drawOptions,
        highlightAtomLists=None,
        highlightBondLists=None,
    )

    return molecule_image, score_df, new_path



with gr.Blocks() as demo:
    with gr.Row():
        with gr.Column(scale=1):
            gr.Markdown("# DrugGEN: Target Centric De Novo Design of Drug Candidate Molecules with Graph Generative Deep Adversarial Networks")
            with gr.Row():
                gr.Markdown("[![arXiv](https://img.shields.io/badge/arXiv-2302.07868-b31b1b.svg)](https://arxiv.org/abs/2302.07868)")
                gr.Markdown("[![github-repository](https://img.shields.io/badge/GitHub-black?logo=github)](https://github.com/HUBioDataLab/DrugGEN)")
            
            with gr.Accordion("Expand to display information about models", open=False):
                gr.Markdown("""
### Model Variations
- **DrugGEN** is the default model. The input of the generator is the real molecules (ChEMBL) dataset (to ease the learning process) and the discriminator compares the generated molecules with the real inhibitors of the given target protein.
- **NoTarget** is the non-target-specific version of DrugGEN. This model only focuses on learning the chemical properties from the ChEMBL training dataset.
        """)
            model_name = gr.Radio(
                choices=("DrugGEN", "NoTarget"),
                value="DrugGEN",
                label="Select a model to make inference",
                info=" DrugGEN model design molecules to target the AKT1 protein"
            )

            num_molecules = gr.Number(
                label="Number of molecules to generate",
                precision=0, # integer input
                minimum=1,
                value=1000,
                maximum=10_000,
            )
            seed_num = gr.Number(
                label="RNG seed value (can be used for reproducibility):",
                precision=0, # integer input
                minimum=0,
                value=42,
            )

            submit_button = gr.Button(
                value="Start Generating"
            )

        with gr.Column(scale=2):
            scores_df = gr.Dataframe(
                label="Scores",
                headers=["Runtime (seconds)", "Validity", "Uniqueness", "Novelty (Train)", "Novelty (Inference)", "Novelty (AKT)", "MaxLen", "MeanAtomType", "SNN (ChEMBL)", "SNN (AKT)"],
            )
            file_download = gr.File(
                label="Click to download generated molecules",
            )
            image_output = gr.Image(
                label="Structures of randomly selected 12 de novo molecules from the inference set:"
            )


    submit_button.click(function, inputs=[model_name, num_molecules, seed_num], outputs=[image_output, scores_df, file_download], api_name="inference")
#demo.queue(concurrency_count=1)
demo.queue()
demo.launch()